Abstract: A distributed learning system of a vehicle includes: a control module configured to: control a plant of the vehicle using a policy; send signals to a learning module including information on an impact of the control on the plant; and selectively control the plant using exploratory control; and the learning module, where the learning module is separate from the control module and is configured to selectively update the policy based on (a) the signals from the control module, (b) state parameters resulting from the control of the plant using the policy, and (c) performance feedback determined based on the control of the plant using the policy and the selective control of the plant using exploratory control, where the control module is configured to receive the exploratory control from the learning module.

Abstract: A system in a vehicle includes memory to store driving history and charging history of the vehicle. The system also includes a processor to obtain a predicted battery life of one or more battery packs of the vehicle based on the driving history and the charging history, obtain a targeted battery life of a user indicating a mileage goal for a specific charge capacity of the one or more battery packs, and determine a difference between the predicted battery life and the targeted battery life. The processor solves an optimization problem to determine a future charging strategy to achieve the targeted battery life and controls an onboard charging system or an external charger based on the future charging strategy or controls routing or navigation based on the future charging strategy.

Abstract: A system for control of a battery system includes a processor connected to the battery system and configured to perform, in real time during a charging process, acquiring a set of charging parameter measurements, estimating a dynamic performance variable related to an electrochemical phenomenon occurring within the battery system during the charging process, and selecting a stored charging profile from a stored relation based on the charging parameter measurements. The processor is further configured to perform applying a charging current to the battery system based on the selected stored charging profile, inputting the charging parameter measurements, the stored charging profile and the stored relation to a learning agent, and evaluating the stored charging profile according to a reward-based learning process, the learning process including estimating a performance value associated with the stored charging profile.

Abstract: A system for control of a battery system includes a processor electrically connected to the battery system. The processor is configured to perform, in real time during a charging process, acquiring a set of charging parameter measurements, and estimating a dynamic performance variable in real time, the dynamic performance variable related to an electrochemical phenomenon occurring within the battery system during the charging process. The processor is also configured to perform, in real time during the charging process, determining a charging limit based on the dynamic performance variable and a model of the battery system, predicting a future state of the battery system, generating a target current profile based on the future state and the charging limit, the target current profile configured to maintain the dynamic performance variable within the charging limit, and controlling the current applied to the battery system based on the target current profile.

Abstract: A solar loading-based system includes a memory, a disturbance prediction module, a cabin temperature estimation module and a thermal control module. The memory stores a cabin thermal load model of an interior cabin of a host vehicle and a solar load prediction model. The disturbance prediction module: receives signals indicative of states of cabin thermal actuators and comfort metrics; and predicts an effect of solar loading over a known portion of a predicted route including predicting cabin temperatures based on the solar load prediction model, the states of the cabin thermal actuators, and the comfort metrics. The cabin temperature estimation module, based on the cabin thermal load model, determines a first comfort metric based on the predicted cabin temperatures. The thermal control module controls cabin thermal actuators to adjust cabin states, including the first comfort metric, to respective target values based on the predicted effect of solar loading.

Abstract: A vehicle, system and a method of predicting a thermal runaway event in a battery pack. The vehicle includes a battery pack having a plurality of battery cells. The system includes a sensor and a processor. The measures a parameter of a battery cell of a battery pack of the vehicle. The processor is configured to determine a charging response of the battery cell from the parameter, determine a likelihood of the thermal runaway event from the charging response, and control an operation of the vehicle to prevent the thermal runaway event based on the likelihood.

Abstract: A system and method for predicting a health of a battery. The system includes a sensor and a processor. The sensor is configured to obtain a battery data indicative of a parameter of the battery. A plurality of scores for the battery data is determined, and the battery data is partitioned into a plurality of subsets for which the scores have a different behavior for each of the subsets. The processor partitions the battery data into a plurality of subsets, determines a score for each of the plurality of subsets, wherein each score is related to the health of the battery, generates an overall score from the scores from each of the subsets, and predicts the health of the battery from the overall score.

Abstract: A computer-implemented method for predicting a quality of a battery includes receiving a first battery measurement data for a first duration of soaking the battery, the first duration shorter than or equal to the soaking. The method further includes computing a plurality of features based on the first battery measurement data. The method further includes predicting, based on the plurality of features, a state of the battery after completion of the soaking. The method further includes outputting suitability of the quality of the battery based on the state of the battery as predicted.

Abstract: A vehicle, system and a method of predicting a thermal runaway event in a battery pack. The vehicle includes a battery pack having a plurality of battery cells. The system includes a sensor and a processor. The measures a parameter of a battery cell of a battery pack of the vehicle. The processor is configured to determine a charging response of the battery cell from the parameter, determine a likelihood of the thermal runaway event from the charging response, and control an operation of the vehicle to prevent the thermal runaway event based on the likelihood.

Abstract: A vehicle operates a system and method of predicting a thermal runaway event in a battery pack of the vehicle. The battery pack includes a battery cell. The sensor obtains measurements of a parameter of the battery cell at a plurality of times. The processor is configured to determine a value of at least one feature of the battery cell from the measurements of the parameter, determine a likelihood of the thermal runaway event from the value of the at least one feature, and take an action to prevent the thermal runaway event from occurring based on the likelihood.

Abstract: A system in a vehicle includes memory to store driving history and charging history of the vehicle. The system also includes a processor to obtain a predicted battery life of one or more battery packs of the vehicle based on the driving history and the charging history, obtain a targeted battery life of a user indicating a mileage goal for a specific charge capacity of the one or more battery packs, and determine a difference between the predicted battery life and the targeted battery life. The processor solves an optimization problem to determine a future charging strategy to achieve the targeted battery life and controls an onboard charging system or an external charger based on the future charging strategy or controls routing or navigation based on the future charging strategy.

Abstract: A solar loading-based system includes a memory, a disturbance prediction module, a cabin temperature estimation module and a thermal control module. The memory stores a cabin thermal load model of an interior cabin of a host vehicle and a solar load prediction model. The disturbance prediction module: receives signals indicative of states of cabin thermal actuators and comfort metrics; and predicts an effect of solar loading over a known portion of a predicted route including predicting cabin temperatures based on the solar load prediction model, the states of the cabin thermal actuators, and the comfort metrics. The cabin temperature estimation module, based on the cabin thermal load model, determines a first comfort metric based on the predicted cabin temperatures. The thermal control module controls cabin thermal actuators to adjust cabin states, including the first comfort metric, to respective target values based on the predicted effect of solar loading.

Abstract: A solar loading-based system includes a memory, a solar load prediction module, and an eco-routing module. The memory is configured to store map information and environment information. The solar load prediction module is configured, based on the map information and the environment information, to (i) determine a route of a host vehicle, (ii) predict solar loading on the host vehicle along the route, and (iii) predict an amount of energy to be consumed by the host vehicle over the route based on the predicted solar loading. The eco-routing module is configured, based on the amount of energy to be consumed by the host vehicle over the route, to at least one of (i) determine whether to follow the route, or (ii) inform a user of the route and the predicted amount of energy to be consumed over the route.

Abstract: A distributed learning system of a vehicle includes: a control module configured to: control a plant of the vehicle using a policy; send signals to a learning module including information on an impact of the control on the plant; and selectively control the plant using exploratory control; and the learning module, where the learning module is separate from the control module and is configured to selectively update the policy based on (a) the signals from the control module, (b) state parameters resulting from the control of the plant using the policy, and (c) performance feedback determined based on the control of the plant using the policy and the selective control of the plant using exploratory control, where the control module is configured to receive the exploratory control from the learning module.

Abstract: A computer for an energy management system of an electric vehicle includes a processor. The computer further includes a memory including instructions such that the processor is programmed to determine a value function V based on a plurality of actions U in a plurality of states S. The processor is further programmed to select an action associated with a highest reward value at a current state S. The action U is an HVAC subsystem variable. The state S is a traction power drawn from a rechargeable energy storage system (RESS) to operate a traction subsystem, a base power input drawn from the RESS to operate an HVAC subsystem, a nominal reference cabin heat input set-point determined by the local HVAC processor, an acceleration of the electric vehicle, a current vehicle speed, an average vehicle speed, and a calibrated average vehicle speed estimate.

Abstract: A method for generating energy-optimized travel routes for a motor vehicle includes one or more of the following: receiving an origin destination (OD) of the motor vehicle and an encrypted energy consumption database of the motor vehicle; generating N candidate routes for the OD; evaluating encrypted energy consumption over a route using an encrypted energy consumption database; applying at least one of homomorphic addition function or homomorphic multiplication function to the encrypted energy consumption data; and returning N candidate routes and their encrypted energy consumption to a client.

Abstract: A control system of a vehicle includes: a target speed module configured to, using a parametric driver model and based on first driver parameters, second driver parameters, and vehicle parameters, determine a target vehicle speed trajectory for a future predetermined period; a driver parameters module configured to determine the first driver parameters based on conditions within a predetermined distance in front of the vehicle; and a control module configured to adjust at least one actuator of the vehicle based on the target vehicle speed trajectory and a present vehicle speed.

Abstract: A system includes a processor and a memory storing instructions which when executed by the processor configure the processor to receive data from a plurality of sensors in a vehicle, the sensors including a Global Navigation Satellite System receiver, accelerometers, gyroscopes, and magnetometers; and to determine a location of the vehicle and a direction of motion of the vehicle based on the data and a history of locations of the vehicle in prior N seconds, where N is a number greater than 0. The instructions configure the processor to determine a permitted driving direction for the vehicle based on the location of the vehicle and a map database; and to detect whether the vehicle is moving in a wrong direction based on the permitted driving direction and the direction of motion of the vehicle.

Abstract: A method for generating energy-optimized travel routes for a motor vehicle includes one or more of the following: receiving an origin destination (OD) of the motor vehicle and an encrypted energy consumption database of the motor vehicle; generating N candidate routes for the OD; evaluating encrypted energy consumption over a route using an encrypted energy consumption database; applying at least one of homomorphic addition function or homomorphic multiplication function to the encrypted energy consumption data; and returning N candidate routes and their encrypted energy consumption to a client.

Abstract: A computer for an energy management system of an electric vehicle includes a processor. The computer further includes a memory including instructions such that the processor is programmed to determine a value function V based on a plurality of actions U in a plurality of states S. The processor is further programmed to select an action associated with a highest reward value at a current state S. The action U is an HVAC subsystem variable. The state S is a traction power drawn from a rechargeable energy storage system (RESS) to operate a traction subsystem, a base power input drawn from the RESS to operate an HVAC subsystem, a nominal reference cabin heat input set-point determined by the local HVAC processor, an acceleration of the electric vehicle, a current vehicle speed, an average vehicle speed, and a calibrated average vehicle speed estimate.